Using a state-space population model to detect age-dependent species interactions
Models that incorporate species interactions and their effects on the dynamics of commercially important fish stocks are needed to better understand the importance of ecological interactions and to facilitate sustainable fisheries. We developed a dynamic age-structured population model for the North...
| Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
|---|---|
| Main Authors: | , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Canadian Science Publishing
2016
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1139/cjfas-2015-0004 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2015-0004 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2015-0004 |
| Summary: | Models that incorporate species interactions and their effects on the dynamics of commercially important fish stocks are needed to better understand the importance of ecological interactions and to facilitate sustainable fisheries. We developed a dynamic age-structured population model for the Northeast Arctic stock of Atlantic haddock (Melanogrammus aeglefinus) based on scientific survey and commercial landings data. Our goal was to investigate climate effects and ecological interactions within the haddock food web. A Bayesian state-space framework was used to separate information from ecological noise and observation error. Our results indicate significant impacts of species interactions on haddock dynamics. Haddock survival was associated with biomass indices of cod (Gadus morhua) (negative effect) and capelin (Mallotus villosus) (positive effect). The latter may reflect lower predation by predators such as marine mammals at high capelin biomass. We further detect weak density dependence in the survival of young haddock and a convex relationship between haddock abundance and the scientific survey indices. Our findings highlight the importance of considering natural resources as part of an ecosystem with its diverse interactions both within and between species. This study shows that it is possible to detect ecological interactions with a population model based on noisy data. |
|---|